We already had reproducibility issues in science. With such models which allow to produce hundreds of "novel" results in one paper, how can we properly keep up in checking all the produced data is correct? This is a real challenge.

Interesting research to determine how models relate to each other. This becomes especially important as the use of synthetic data increases.

Interesting research, this gives a few hints at building tools to ensure some more transparency at the ideologies pushed by models. They're not unbiased, that much we know, characterising the biases are thus important.

Interesting new proof on the relationships between P and PSPACE. Let's see where this leads.

Or how the current neural networks obsession is poisoning scientific fields. There was already a reproducibility crisis going on and it looks like it's been getting worse. The incentives are clearly wrong and that shows.

This is very interesting research. This confirms that LLMs can't be trusted on any output they make about their own inference. The example about simple maths is particularly striking, the real inference and what it outputs if you ask about its inference process are completely different.

Now for the topic dearest to my heart: It looks like there's some form of concept graph hiding in there which is reapplied across languages. Now we don't know if a particular language influences that graph. I don't expect the current research to explore this question yet, but looking forward to someone tackling it.

This is interesting research. It shows nice prospects for WebAssembly future as a virtualization and portability technology. I don't think we'll see all of the claims in the discussion section realized though.

I like this kind of research as it also says something about our own cognition. The results comparing two models and improving them are fascinating.

Interesting study even though it bears some important limitations. Still it seems to indicate that one shouldn't rest on its laurels and keep practicing cognitive skills even when older (actually might have to get started in the 20s latest).

Friendly reminder that AI was also supposed to be a field about studying cognition... There's so many things we still don't understand that the whole "make it bigger and it'll be smart" obsession looks like it's creating missed opportunities to understand ourselves better.

Interesting research, looking forward to the follow ups to see how it evolves over time. For sure the number of issues is way to high still to make trustworthy systems around search and news.

This is clearly pointing in the direction of UX challenges around LLM uses. For some tasks the user's critical thinking must be fostered otherwise bad decisions will ensue.

Wondering what a Ph.D. is about? This is a good illustrated summary.

We're still struggling about how to modularize our code. Sometimes we should go back to the basics, this paper by Parnas from 1972 basically gave us the code insights needs to modularize programs properly.

Interesting research about feasibility of making compilers parallelized on the GPU. I wonder how far this will go.

Fascinating research about side-channel attacks. Learned a lot about them and website fingerprinting here. Also interesting the explanations of how the use of machine learning models can actually get in the way of proper understanding of the side-channel really used by an attack which can prevent developing actually useful counter-measures.

Very interesting research. Looks like we're slowly moving away from the "language and thinking are intertwined" hypothesis. This is probably the last straw for Chomsky's theory of language. It served us well but neuroscience points that it's time to leave it behind.

Now this is an interesting paper. Neurosymbolic approaches are starting to go somewhere now. This is definitely helped by the NLP abilities of LLMs (which should be used only for that). The natural language to Prolog idea makes sense, now it needs to be more reliable. I'd be curious to know how many times the multiple-try path is exercised (the paper doesn't quite focus on that). More research is required obviously.

Now the impact seems clear and this is mostly bad news. This reduces the production of public knowledge so everyone looses. Ironically it also means less public knowledge available to train new models. At some point their only venue to fine tune their models will be user profiling which will be private... I've a hard time seeing how we won't end up stuck with another surveillance apparatus providing access to models running on outdated knowledge. This will lock so many behaviors and decisions in place.

Of course I recommend reading the actual research paper. This article is a good summary of the consequences though. LLMs definitely can't be trusted with formal reasoning including basic maths. This is a flaw in the way they are built, the bath forward is likely merging symbolic and sub-symbolic approaches.